{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import shutil\n",
    "from torchvision import transforms\n",
    "from torchvision import models\n",
    "import torch\n",
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "from torch.optim import lr_scheduler\n",
    "from torch import optim\n",
    "from torchvision.datasets import ImageFolder\n",
    "from torchvision.utils import make_grid\n",
    "import time\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def imshow(inp):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Peep look into the downloaded data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```\n",
    "    chapter3/\n",
    "        dogsandcats/\n",
    "            train/\n",
    "                dog.183.jpg\n",
    "                cat.2.jpg\n",
    "                cat.17.jpg\n",
    "                dog.186.jpg\n",
    "                cat.27.jpg\n",
    "                dog.193.jpg\n",
    "  \n",
    "\n",
    "```\n",
    "\n",
    "```\n",
    "    chapter3/\n",
    "        dogsandcats/\n",
    "            train/\n",
    "                dog/\n",
    "                    dog.183.jpg\n",
    "                    dog.186.jpg\n",
    "                    dog.193.jpg\n",
    "                cat/\n",
    "                    cat.17.jpg\n",
    "                    cat.2.jpg\n",
    "                    cat.27.jpg\n",
    "            valid/\n",
    "                dog/\n",
    "                    dog.173.jpg\n",
    "                    dog.156.jpg\n",
    "                    dog.123.jpg\n",
    "                cat/\n",
    "                    cat.172.jpg\n",
    "                    cat.20.jpg\n",
    "                    cat.21.jpg\n",
    "\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Create validation data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "path = '../chapter3/dogsandcats/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "files = glob(os.path.join(path,'*/*.jpg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no of images 0\n"
     ]
    }
   ],
   "source": [
    "print(f'Total no of images {len(files)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "no_of_images = 25000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "no_of_images = len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_of_images*0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "shuffle = np.random.permutation(no_of_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "os.mkdir(os.path.join(path,'valid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true,
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for t in ['train','valid']:\n",
    "    for folder in ['dog/','cat/']:\n",
    "        os.mkdir(os.path.join(path,t,folder)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "for i in shuffle[:2000]:\n",
    "    #shutil.copyfile(files[i],'../chapter3/dogsandcats/valid/')\n",
    "    folder = files[i].split('/')[-1].split('.')[0]\n",
    "    image = files[i].split('/')[-1]\n",
    "    os.rename(files[i],os.path.join(path,'valid',folder,image))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "for i in shuffle[2000:]:\n",
    "    #shutil.copyfile(files[i],'../chapter3/dogsandcats/valid/')\n",
    "    folder = files[i].split('/')[-1].split('.')[0]\n",
    "    image = files[i].split('/')[-1]\n",
    "    os.rename(files[i],os.path.join(path,'train',folder,image))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check if GPU is present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "if torch.cuda.is_available():\n",
    "    is_cuda = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Load data into PyTorch tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_transform = transforms.Compose([transforms.Resize((224,224))\n",
    "                                       ,transforms.ToTensor()\n",
    "                                       ,transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n",
    "train = ImageFolder('dogsandcats/train/',simple_transform)\n",
    "valid = ImageFolder('dogsandcats/valid/',simple_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cat': 0, 'dog': 1}\n",
      "['cat', 'dog']\n"
     ]
    }
   ],
   "source": [
    "print(train.class_to_idx)\n",
    "print(train.classes) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YK1oLUqRl4HmxUBVFVCgxtYXfim0coesXFYSh1ovy8OCKIKW0mnTWyE7Fmfht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\nLFhwhP8Dsg1AlSaQ0T0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f343d4b24a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(train[50][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create data generators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data_gen = torch.utils.data.DataLoader(train,shuffle=True,batch_size=64,num_workers=3)\n",
    "valid_data_gen = torch.utils.data.DataLoader(valid,batch_size=64,num_workers=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset_sizes = {'train':len(train_data_gen.dataset),'valid':len(valid_data_gen.dataset)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataloaders = {'train':train_data_gen,'valid':valid_data_gen}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ft = models.resnet18(pretrained=True)\n",
    "num_ftrs = model_ft.fc.in_features\n",
    "model_ft.fc = nn.Linear(num_ftrs, 2)\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    model_ft = model_ft.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d (3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (relu): ReLU(inplace)\n",
       "  (maxpool): MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1))\n",
       "  (layer1): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d (64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d (64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d (128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d (128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d (256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d (256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0, ceil_mode=False, count_include_pad=True)\n",
       "  (fc): Linear(in_features=512, out_features=2)\n",
       ")"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Loss and Optimizer\n",
    "learning_rate = 0.001\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=5):\n",
    "    since = time.time()\n",
    "\n",
    "    best_model_wts = model.state_dict()\n",
    "    best_acc = 0.0\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in ['train', 'valid']:\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "                model.train(True)  # Set model to training mode\n",
    "            else:\n",
    "                model.train(False)  # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for data in dataloaders[phase]:\n",
    "                # get the inputs\n",
    "                inputs, labels = data\n",
    "\n",
    "                # wrap them in Variable\n",
    "                if torch.cuda.is_available():\n",
    "                    inputs = Variable(inputs.cuda())\n",
    "                    labels = Variable(labels.cuda())\n",
    "                else:\n",
    "                    inputs, labels = Variable(inputs), Variable(labels)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "                \n",
    "                # forward\n",
    "                outputs = model(inputs)\n",
    "                _, preds = torch.max(outputs.data, 1)\n",
    "                loss = criterion(outputs, labels)\n",
    "\n",
    "                # backward + optimize only if in training phase\n",
    "                if phase == 'train':\n",
    "                    loss.backward()\n",
    "                    optimizer.step()\n",
    "\n",
    "                # statistics\n",
    "                running_loss += loss.data[0]\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects / dataset_sizes[phase]\n",
    "\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "\n",
    "            # deep copy the model\n",
    "            if phase == 'valid' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = model.state_dict()\n",
    "\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/1\n",
      "----------\n",
      "train Loss: 0.0013 Acc: 0.9666\n",
      "valid Loss: 0.0006 Acc: 0.9875\n",
      "\n",
      "Epoch 1/1\n",
      "----------\n",
      "train Loss: 0.0005 Acc: 0.9887\n",
      "valid Loss: 0.0005 Acc: 0.9890\n",
      "\n",
      "Training complete in 1m 49s\n",
      "Best val Acc: 0.989000\n"
     ]
    }
   ],
   "source": [
    "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
    "                       num_epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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